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Generalizability in mixed models: Lessons from corpus linguistics
Freek Van de Velde1, Stefano De Pascale1, Dirk Speelman1
1Department of Linguistics, KU Leuven, Blijde Inkomststraat 21/3308, BE-3000Leuven, Belgium. freek.vandevelde@kuleuven.be stefano.depascale@kuleuven.be dirk.speelman@kuleuven.behttps://www.arts.kuleuven.be/ling/qlvl/people/pages/00039016; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00102617; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00013279.
Corpus linguistic methods improve psychological research generalizability using natural data. However, researchers must address potential skews in language data and participants to ensure reliable findings.
Area of Science:
- Psycholinguistics
- Corpus Linguistics
- Psychological Research Methods
Background:
- Controlled laboratory experiments in psychology and psycholinguistics often face generalizability challenges.
- Reliance on artificial settings may limit the applicability of findings to real-world language use.
Purpose of the Study:
- To explore how corpus linguistic methods can enhance the generalizability of psychological research.
- To identify and address potential limitations introduced by corpus data.
Main Methods:
- Utilizing corpus linguistic approaches to analyze natural language data.
- Examining potential skews in lexemes (words) and language users within corpus datasets.
Main Results:
- Corpus linguistics offers a valuable alternative to traditional experimental designs for improving ecological validity.
- Identified specific types of skews in corpus data that can impact research outcomes.
Conclusions:
- Corpus methods can significantly bolster the control and generalizability of psychological and psycholinguistic studies.
- Implementing strategies to mitigate data skews is crucial for maximizing the benefits of corpus-based research.
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